Scaling Automation

While RPA has been around for more than a decade, the majority of organizations are still challenged with achieving scale with this technology. According to most researchers, scale with RPA is defined as having at least 50 bots in production use, and their research suggests that less than 5% of organizations achieve this level of adoption.

 

Why is this so? Why do organizations have such apparent difficulty in leveraging digital labor across their enterprise? IRPA’s “Scaling Automation” service provides specific guidance on how to break through known barriers to adoption, and achieving the same defensible, structural, strategic advantage that Intelligent Automation can provide.

Chris Surdak, JD

Senior Advisor to IRPA AI

Former Gartner Executive Partner &  AI & Automation Practice Lead, EY

Chris is an industry-recognized expert with over 30 years of experience in Artificial Intelligence Automation, Mobility, Social Media and Analytics, Big Data, Information Security, Regulatory Compliance,  and Cloud Computing. He began his career with Lockheed Martin Astrospace and has led automation & AI practices at Gartner and EY, currently working as a researcher specializing in Digital Transformation.

Chris is also the author of  “The Care and Feeding of BOTS”, “Jerk: The Digital Transformation cookbook” and “Data Crush”.

“Just for Jerks” Video Series

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Active Blogs Team !

What Would Einstein Do? The Ethical Conundrums of Artificial Intelligence

This week I presented at the Sub-Four eDiscovery and Information Governance in the legal industry, hosted at Pelican Hill Resort. While these topics may strike some as exciting as watching paint dry, others of us find these topics to be interesting, relevant, and sometimes even critical to our careers and our lives. In the session I moderated, we discussed the implications of Artificial Intelligence on the legal profession and whether we believe AI will have a meaningful impact on the practice of law. The discussion rapidly detoured to a focus on the ethical implications of AI in the law, and I wanted to revisit the discussion here for posterity.

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Do You Have Data Diabetes?

Organizations constantly lament that they have too much data, not enough data, or the wrong data, often simultaneously. They need data to thrive and create

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